Data fusion/data mining-based architecture for condition-based ...

International Journal of Production Research, Vol. 44, No. 14, 15 July 2006, 2869–2887

Data fusion/data mining-based architecture for condition-based maintenance D. RAHEJAy, J. LLINASy, R. NAGI*y and C. ROMANOWSKIz yDepartment of Industrial Engineering, University at Buffalo (SUNY), 342 Bell Hall, Center for Multisource Information Fusion, Amherst, USA zCenter for Multidisciplinary Studies, College of Applied Science and Technology, 2219 Eastman Building, Rochester Institute of Technology, Rochester, NY 14623, USA

(Revision received February 2006) Condition-based maintenance (CBM) is a maintenance philosophy wherein equipment repair or replacement decisions are based on the current and projected future health of the equipment. The constituents and sub-processes within CBM include sensors and signal processing techniques that provide the mechanism for condition monitoring, and decision support models. Since past research has been dominated by condition monitoring techniques for specific applications, the maintenance community lacks a generic CBM architecture that would be relevant across different domains. This paper attempts to fulfil that need by proposing a combined data fusion/data mining-based architecture for CBM. Data fusion, which is extensively used in defence applications, is an automated process of combining information from several sources in order to make decisions regarding the state of an object. Data mining seeks unknown patterns and relationships in large data sets; the methodology is used to support data fusion and model generation at several levels. In the architecture, methods from both these domains analyse CBM data to determine the overall condition or health of a machine. This information is then used by a predictive maintenance model to determine the best course of action for maintaining critical equipment. Keywords: Condition-based maintenance; Data fusion; Data mining

1. Introduction A survey of existing condition-based maintenance (CBM) literature (Raheja et al. 2000) suggests that today’s approaches to CBM system design are extremely specific in nature, i.e. what would usually be called ‘point designs’. Research has focused mainly on condition monitoring techniques for specific applications. Although this has helped increase the awareness of CBM in the maintenance community, the lack of a generic architecture has meant that each domain or area has its own interpretation of CBM, one that may not be compatible with the requirements of other applications. The purpose of this paper is to propose an integrated

*Corresponding author. Email: [email protected] International Journal of Production Research ISSN 0020–7543 print/ISSN 1366–588X online ß 2006 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/00207540600654509

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data-mining/data-fusion-based architecture that is relevant across different application areas. This effort will fulfil the following needs: . The need for a consistent view of the CBM process, one which could subsequently be used as a template by different applications for designing CBM systems specific to their requirements. . The need for a holistic or integrated approach that depicts the propagation and integration of information required at various decision-making levels of an overall CBM strategy. This approach is different from focusing solely on CBM for specific components rather than the system as a whole. . The need to integrate new and powerful data analysis techniques into general maintenance practice. 2. Objectives and overall approach for defining a general architecture IEEE Std. 610.12A-1990 (US Department of Defense 1998) defines architecture as ‘the structures or components, their relationships, and the principles and guidelines governing their design and evolution over time’. This definition has been used herein as a foundation for defining our approach, replacing ‘structures or components’ with ‘CBM modules or processes’. The technical objectives of defining the architecture are as follows: . Introduce a methodology for converting high-level organizational goals into CBM goals. CBM goals could be generic, pertaining to a system (e.g. a machine) or specific, pertaining to a subsystem (e.g. a particular component of the machine). The goals could be used to do the following: . Develop component descriptions (‘parts tree’ formulation). . Identify failure modes or ‘phenomena’ for the relevant components (‘fault tree’ formulation). . Propose the application of data-fusion and data-mining processes for CBM, in addition to condition monitoring techniques. . Understand the interrelationships of the various architectural modules. The modules are functions or processes that have various interrelationships which help in accomplishing the overall objective. The present paper also intends to investigate how the modules complement each other to permit the development of a general architectural approach. Such an approach will provide a coherent decision support framework on which sound maintenance decisions or policies can be based. The overall objective is not to provide a detailed specification of the hardware or software that may be required for the implementation of such a system, but rather a conceptual description or explanation of the modules that satisfies the above requirements. 2.1 Recent data-fusion and data-mining approaches in CBM Incorporating data-fusion and data-mining technologies in a CBM architecture adds significant functionality to a system. The general model for the data fusion domain (Steinberg et al. 1999) shows many levels of increasingly sophisticated assessments,

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from sensor-level (raw) data analysis to situation and impact analysis. These levels also represent increasingly difficult problems of estimation and prediction, and align nicely with the CBM process hierarchy discussed below in this section (Hall 1999). Work in this area of combining data fusion with CBM is fairly recent (Hall and Kasmala 1996, Garga and Hall 1999, Starr et al. 2002a, Djurdjanovic et al. 2003), domain-specific (Reichard et al. 2000, Krok and Ashby 2002, Casoetto et al. 2003, Starr et al. 2002b), and dominated by military applications (Hegner and Nemarich 1997, Garga and Hall 1999, Ashby and Scheuren 2000, Hadden et al. 2000, Brotherton and Mackey 2001, Price et al. 2001, Orsagh and Sheldon 2003, Reichard et al. 2003). Data mining supports data fusion at several levels, and can help to answer many of the questions inherent in CBM applications. Because data mining searches historical data for interesting and previously unknown patterns, these methods can help determine the following: . . . . .

What machines should be monitored? How often should these machines be monitored? What types of failures occur on a particular machine? What are the warning signs of an impending failure? How does one integrate maintenance needs with production/availability?

Efforts to incorporate data mining with CBM have also been fairly recent and application-specific (e.g. Olsson et al. 2004, Wu et al. 2004). Combining data fusion and data mining with CBM has, to the best of the present authors’ knowledge, been proposed only recently by Waltz (1998) and Wu et al. (2004). However, the integration of data mining with data fusion is limited, and the architectures loosely specified.

2.2 CBM systems The leading organizations developing standards and systems architectures in CBM research are the Machine Information Management Open System Alliances (MIMOSA) and Open System Architecture for Condition based Maintenance (OSACBM). OSA-CBM proposed a CBM system comprising seven modules (Bengtsson, 2004): . Sensor module: provides sensor data. . Signal-processing module: receives the sensor data and transforms it into an analysable output. . Condition-monitoring module: receives data from both the sensor module and the signal-processing module, and possibly from other conditionmonitoring modules. These data are analysed and compared with expected values and ranges. This module may also generate alerts if the data values exceed preset limits. . Health assessment module: receives data from other health-assessment modules and from the condition-monitoring module. These data are analysed to determine if any degradation in system condition has occurred. The module may report diagnostic results and fault hypotheses.

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. Prognostic module: gathers data and information from all upstream modules and attempts to forecast changes in the monitored system’s health. . Decision support module: uses data and information from all upstream modules to make recommendations that mitigate system failure risks. . Presentation module: user interface for all modules. Bengtsson (2004) suggests existing IEEE and ISO standards, such as ISO 13373-1 (focusing on machine vibration data collection and analysis) and MIMOSA’s Common Relation Information Schema (CRIS) that could guide the development of each of these modules. These standards are completely compatible with the present proposed system as well; maintaining an open source environment enables more efficient integration of system components and makes upgrading easier (Thurston and Lebold 2001).

3. General overview of the architecture The skeleton or framework of the architecture is based on the ‘CBM hierarchy’ (Hall 1999), which partitions the system of interest into six levels: platform, system, subsystem, component, element and material. Generic CBM goals or requirements are usually defined for the platform level. These generic goals are broken down into increasingly specific goals/requirements for the lower levels. The need for this breakdown is best explained using a representative example. Consider a CBM hierarchy with a machine as the ‘entity’ at the platform level. The machine is made up of different components and their sub-assemblies. These constitute the various sub-levels (below the platform level) of the hierarchy. Since the ‘condition’ or ‘health’ of the machine is dependent on its components, any maintenance policy for the machine (which is at the platform level) will necessarily have to incorporate the maintenance needs of the components at the sub-levels. The same argument applies for the subassemblies of the components. Thus, at any level of the hierarchy, there will exist the following: . . . .

Information that identifies the component(s). Information about the failure modes for each of the components. CBM goals pertaining to each component/group of components. Component health information (sensor measurements or estimates) that relates to the state of the failure mode.

Overall machine health is determined by a combined estimate of the health of all its parts and components. Similarly, a particular component’s health is determined by a combined estimate of its subcomponents’ health. The estimates obtained at each hierarchical level are inputs to a predictive model that set the maintenance policies for that level. Note that these policies must be compatible with the previously set CBM goals. Figure 1 is a pictorial representation of the integrated data-mining/data-fusion CBM architecture. Several important inputs are necessary to the efficient operation of the CBM process. These inputs are discussed first. The paper then describes how the various modules interact with each other to provide information at a specific level of the hierarchy. Also, at various points throughout the discussion, a vibration


Figure 1.

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Proposed data-mining/data-fusion architecture for condition-based maintenance.

study of a helicopter aft transmission and a gear crack example is used to illustrate how this novel hybrid approach can be implemented.

3.1 Input to the architecture: formation of CBM goals High-level organizational goals form the highest-level input to the architecture. These goals stem from the company’s overall business strategy or objectives. An example of such a goal is ‘Reduce product cost by 20%’. The organizational goals are then broken down into goals for different corporate functions. As companies recognize the importance of maintenance to their ability to compete, this function is receiving more attention at the organizational level and is increasingly included in high-level planning efforts. A company may have different maintenance-related goals/policies for different machines/systems; CBM is one of several possible approaches. Clearly, CBM-specific goals must be developed for systems to which CBM is being applied. Platform-level goals are the ‘highest-level’ goals within the CBM framework. A possible platform level goal, using the helicopter transmission as an example, is ‘Increase reliability to 98%’. ‘Sublevel’ goals will be derived from the platform level goal(s). These goals form the inputs for the CBM process modules. Figure 2 shows the translation of high-level objectives into CBM requirements and their ‘flow-down’ into the CBM modules.

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Figure 2. Translating high-level organizational goals into condition-based maintenance requirements.

While forming the CBM goals, it is important to keep in mind that the organizational objectives may not always translate into feasible goals for CBM. Feasible goals would be those that can be achieved keeping in mind the failure dynamics of the machine/component for which CBM policy is being designed. There is a two-way interaction of this process with the next steps of parts tree/ fault tree development. The formulation of goals and development of the fault tree could be performed simultaneously because setting ‘realistic’, achievable goals requires a knowledge of the potential failure modes. On the other hand, development of the parts tree/fault tree will take place with respect to a platform-level component with associated CBM goals. Formulating goals for each component (or group of components) at various levels of the hierarchy is required because it is assumed that the failure of the platform-level component can occur only if a component (or group of components) fails at a sub-level. In other words, the maintenance needs of the sub-levels will have to be taken into account, and this requires setting goals at each sub-level in addition to the platform level.

3.2 Input to the architecture: development of a ‘parts tree’ The parts tree identifies the equipment and all the components of the equipment under maintenance. It also maps these components with the CBM hierarchy diagram. In this way, each component can be associated with a particular level of the hierarchy. A representative parts tree for the helicopter transmission (along with the CBM hierarchy mapping) is shown in figure 3, with one branch expanded to the component level.


Figure 3.

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Parts tree.

There is a distinct similarity between the parts tree and a bill-of-material (BOM). A BOM essentially identifies the parts/components of a product along with the required quantities of each part/component (Fogarty and Hoffmann 1983). However, an important difference is that a CBM parts tree contains only those parts that need to be maintained. For example, ‘sheet metal’ may not be a part whose condition is continuously monitored or routinely maintained. Hence, it would not be listed in the parts tree, but would be listed in the BOM. Additionally, a component could be at different ‘levels’ in the parts tree and the BOM mainly because the parts tree is constructed from the perspective of maintenance needs/failure modes, whereas the BOM reflects the assembly structure of the product. In a sense, the parts tree can be thought of as a derivative of the BOM.

3.3 Input to the architecture: development of a ‘fault tree’ Before informed decisions can be made about maintenance requirements at any level in the hierarchy, potential abnormalities that may occur during normal operation must be identified. The fault tree is basically an identification of the ‘pathological’ phenomena that arise from the failure mode(s) associated with the machine/equipment at the platform level. Fault tree development is highly dependent on domain knowledge. A representative fault tree for the helicopter example is depicted in figure 4, with one failure path expanding to the basic event. While designing the fault tree, it must be recognized that some phenomena may be caused by the interaction of two parts. For example, ‘gear tooth failure’ may involve two gears that mesh with each other. Developing the fault tree may not be as simple as associating failure modes with each part/component, since component interaction must also be considered. Another important design issue is the effect of the condition of one component on another. For example, if excessive vibration of the gearbox is caused by ‘gear tooth breakage’, does that affect the condition of other parts of the gearbox, such as the bearing? Answering these design questions is a necessary precursor to actual implementation of the conceptual design.

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Figure 4.

Portion of a helicopter transmission fault tree.

Identification of the failure modes offers a twofold advantage to the CBM engineer. The fault tree helps in the following: . Setting CBM goals. CBM goals are defined for the component(s) at each level. As mentioned above, these goals are derived from the goals at a higher level. Clearly, CBM goal formulation is influenced by the potential failure modes of the components. For example, consider the component level of the parts tree where ‘gear teeth’ is a representative component. A CBM goal for this component could be ‘Reduction of tooth fracture by 25%’. Such a goal can only be set realistically if adequate knowledge of ‘fracture’ (which is a failure mode) is available. . Determining sensing requirements. The fault tree information suggests the types and placement of sensors required to monitor the state of the failure mode. Data-mining methods are a natural fit for the process of generating a fault tree. This task is time-consuming because of its dependence on domain knowledge and subjectmatter experts (SMEs). Given historical data, data-mining methods such as decision trees and association rules can semi-automate the search for failure patterns that may have otherwise escaped notice. While no data-mining method is completely automatic, judicious use of these techniques can save large amounts of time and effort. The parts tree and fault tree identify the parts that need to be maintained along with the associated failure modes. Apart from formulating goals for each level, the parts tree and fault tree are also required for setting up maintenance policies for the constituents of the system. Additionally, the information provided by this module is an important input for the condition-monitoring process.


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3.4 Condition-monitoring module The parts tree identifies the components that need to be maintained and the fault tree identifies the possible ways in which these components can fail. These pieces of information are used to set in motion the process of condition monitoring, continuously gauging the condition of the equipment and its components. The process also encompasses signal processing, which is the term used for converting raw sensor data into a more convenient form for interpretation. There are several examples of condition monitoring programs for specific components (Raheja et al. 2000). Condition monitoring, when viewed as a module of the proposed architecture, consists of a few processes in addition to signal detection and signal processing. The sub-processes within condition monitoring are as follows: . . . .

Identification of ‘observables’. ‘Partitioning’ the observables. Determining sensing requirements. Signal detection and signal processing.

3.4.1 Identification of observables. Any maintenance decision based on the operating condition of an equipment/machine or its components will require parameters or measurements that provide information about the present condition. These parameters will indicate the occurrence of failure modes. It was, therefore, chosen to call them ‘effects’ of the phenomena or observables. In essence, they are outputs from various sensors. Examples of observables could be ‘crack length measurement’ and ‘chip content in oil’. These may be effects of a phenomenon called ‘gear tooth breakage’. Typically, a number of observables are combined to form an estimator. The estimator is then used to gauge the condition of the machine or component at different instances in time. Recall the phrase ‘vital information’ used above; an estimator is that ‘vital information’. Continuing with the example provided above, assume that in order to ascertain ‘gear tooth breakage’, one needs an estimate of ‘crack length’. The observables that provide information about this estimate could then be ‘crack length measurement’ and ‘chip content in oil’.

3.4.2 Partitioning observables and determining the types of measurements. Observables must be separated into two distinct categories: direct and indirect. The terms ‘direct’ and ‘indirect’ warrant some discussion. The paper introduced the relationship between observables and estimators above. The following discussion will build on those concepts. A directly ascertained or measurable observable is defined as that whose value is a direct indicator of the estimate. Thus, if the estimator is ‘crack length’ and if one makes use of a sensor that can provide a measure of the ‘crack length’, then the observable (i.e. the sensor output) is a direct observable. Instead, if an observable needs to be ‘converted’ (using pre-processing) in order for it to be used to ascertain the magnitude of the estimator, the observable is indirect. To make this concept clear, the paper will consider a hypothetical example.

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Assume that one needs to estimate the ‘gear tooth crack length’. Observables will not be needed to form the required estimator. A direct observable would be one that literally measures the crack length (e.g. a camera). An indirectly measured observable could be ‘chip content in oil’. Previously gained knowledge may help one to conclude this fact. There will also have to be a failure model that relates the chip content with the crack length. This is, however, the subject of section 3.7. Here, the example is used to explain the concept of partitioning, or classifying. Partitioning the observables has an obvious impact on the way that sensing requirements are ascertained. Determining sensing requirements consists of sensor selection and design, and is an important part of the architecture. A sensor suite can be designed depending on whether an observable is direct or indirect. Partitioning also allows the CBM engineer to determine the type of measurements (in terms of the units) that need to be recorded so that estimation algorithms (for combining the parameters/measurements) can be developed.

3.4.3 Determining sensing requirements. Determining sensing requirements for measuring observables is an important precursor to signal detection and processing. There are several technical factors that need to be considered (Hegner et al. 1997): . Using the fault tree to determine what failures need to be measured. The fault tree provides information on the subsystems and components involved in failures, but not all failure modes lend themselves to sensor measurement. The engineer must use judgement to determine feasible candidates for sensor monitoring, taking into account the probability of the mode occurring, the criticality of the fault to overall operations, and the difficulty of obtaining adequate, reliable, economically reasonable data from the particular subsystem or component. . Partitioning the observables depending on whether they are ‘direct’ or ‘indirect’. . Determining the type of measurement required in terms of the units of measurement. This is somewhat dependent on the estimation process that will be described below. . ‘Feasible’ locations for sensors (sensor placement). Some of the general factors to be taken into account for sensor selection are as follows: . . . . .

Cost. Reliability. Range and span. Operating environment. Sensitivity.

There will usually be a trade-off between the quality of sensor output and the cost of a sensor. The CBM engineer will have to determine the ‘optimal point’ in this tradeoff depending on the criticality of the measurement and marginal accuracy achieved per dollar expended.


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3.4.4 Data mining in condition-based maintenance subprocesses. Certain CBM activities are greatly benefited by the application of data-mining techniques to available historical data. It has already been discussed how data mining can be used in fault tree development. These methods can also be helpful in sensor placement and in determining alert thresholds. For example, in Romanowski and Nagi (2001), data-mining techniques were applied to vibration monitoring data from the helicopter transmission example. Eight sensors were placed on the transmission and data were collected for seven induced failure modes. The data-mining results successfully identified those sensors giving critical information for specific modes, as well as those sensors that contributed no information for any failure mode. In addition, the resulting decision trees define the range of frequencies characteristic of each type of fault. This application of data mining clearly shows that when historical data are available, incorporating data-mining methods into the planning of CBM activities and elements can save money by eliminating unnecessary sensors and also greatly enhance the capabilities of a CBM system. The condition-monitoring process is responsible for sensing and recording the condition/health of the system. It uses the component/failure mode information from the parts tree/fault tree in order to do the following: . Determine which parameters/observables need to be measured. . Select sensors and determine their placement/location. . Distinguish between direct and indirect observables. 3.5 Data-fusion/data-mining process Data fusion is central to the architecture mainly because of the nature of the present approach that requires information to be combined across different entities and levels in the CBM hierarchy. Note that this module corresponds to the OSA-CBM condition monitoring, health assessment and prognostics modules. The data-fusion and data-mining methods provide learning and reasoning functionalities that can characterize and predict the machine or system states from the available data. This section will explain the various sub-processes within data fusion and how they are relevant to the CBM process. It will also discuss the importance of data mining to both CBM and data fusion. 3.5.1 Alignment. A data-fusion process requires a consistency of measurement units and coordinate systems across data values that need to be combined. It is the alignment function that incorporates various ‘normalization’ processes that put the sensor data streams on a common time and units basis. Recall that this process may also be part of the signal processing, which is a sub-process of condition monitoring. This is an instance where two different modules of the architecture overlap. In some cases, where categorical or ordinal data are also available, data alignment includes the process of integrating the heterogeneous data together in a usable input form for a data-mining or data-fusion algorithm. 3.5.2 Associating observables with phenomena: a data mining application. The paper introduced above the concepts of observables and failure modes or pathological

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Figure 5.

The Association Problem: associating observables with phenomena.

phenomena and the relationship between the two. It is quite possible that the mapping between them may not be one-to-one, i.e. some of the observables may be associated with multiple phenomena. For example, ‘chip content in oil’ could be associated not only with ‘gear tooth breakage’, but also with ‘gear bearing failure’ with a certain probability (figure 5). This complexity is handled by the process of associating an observation with a phenomenon or failure mode, as in the a priori-based association rule-mining method (Agrawal and Srikant 1994). The strength of an association rule is evaluated by the level of support, or confidence, which is usually determined from domain knowledge and the modelling of specific sensor-to-phenomena relationships. Determining the associations and correlating multiple sources of data is one of the initial steps of a data-fusion problem. The sub-tasks of the association/ correlation problem are as follows: . Model generation: feasible sensor data-to-phenomenon possibilities (each feasible association is an hypothesis). . Model evaluation: determines the ‘strength’ of the relationship. . Model selection: determines the optimal sensor data-to-phenomenon association in consideration of the ambiguities present. Although the standard data-fusion language is ‘hypothesis’ instead of ‘model’, it was chosen instead to use the latter to indicate that, in the present proposed architecture, one could advocate the use of a wide range of techniques from traditional statistics to the data-driven methods of machine learning. The addition of data-mining techniques in the data fusion toolbox is proposed as an alternative to using only domain knowledge and past experience for solving the association problem. Data mining will help enrich the hypothesis space and enable the CBM engineer to build models that will identify the phenomena associated with an observable. It will also provide probabilistic measures of support for each of these associations. Additionally, the data-mining algorithm can be designed in such a way that it prunes associations that fall below a particular confidence level. Thus, mining models can be used to aid in determining optimal associations of the sensor data to each phenomenon, and to the respective estimation processes that exploit the sensor data. The specific mining methodologies (e.g. decision trees, association rules, case-based reasoning, etc.) are domain- and data-dependent. To continue with the helicopter transmission example, the data-mining technique of decision trees applied to the fused set of eight sensor streams generated rules correlating frequencies and magnitudes of vibrations to specific failure modes.


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For instance, one such rule associated the values {frequency47900 Hz, sensor 4 magnitude range between 42.83 and 43.28 dB, sensor 7 magnitude540.52 dB, and sensor 8 magnitude range between 42.44 and 43.07 dB} with a cracked idler gear. The probability of idler gear crack (support for this failure mode, based on the historical data) given these values is 92%. The range information is also helpful for determining alarm settings for the monitoring processes. Condition monitoring and data mining form a ‘feedback loop’ wherein data mining uses the condition monitoring information to generate models and hypotheses and identifies the important sensors. This information is then fed back to the condition-monitoring process. There is also scope for active or dynamic data mining (Agrawal and Psaila 1995). In most applications, the mining algorithm operates on static data, i.e. data that have been collected up to a point in time. Dynamic data mining works on data that are continuously changing. The process uses the new information to update its ‘rules’. Every time the rules indicate that a particular threshold has been crossed, an action is triggered. Dynamic data mining is relevant for a CBM application where the condition-monitoring process continuously provides updated information and where threshold values change as the machine ages. Once the data are aligned and the association/correlation process completed, the next step is the estimation of system health. Estimation algorithms approximate the nature and degree of the phenomena or failure modes.

3.6 Estimation process The estimation process is central to data fusion. In this stage, the information extracted from the data is combined; in this case, because we have conceptualized the CBM problem as a multi-level problem, the information is fused across the hierarchical levels. The estimation engine is an information model or algorithm that combines these different data sets according to some multicriteria function specified by the application. Typically, the output of this process is a fused estimate that represents the health of the component(s) up to a chosen level of the hierarchy. This estimate is used by a predictive, decision-making model in order to calculate remaining useful life, based on which maintenance decisions can be made. Estimation techniques, such as data-mining techniques, are highly domaindependent. They are symbolic reasoning methods, variants of automated or approximate reasoning methods such as fuzzy logic, neural networks, Bayesian reasoning, Dempster-Shafer theory or analytic hierarchy (Rogova and Llinas 1998). As an example of an estimation at the component level, suppose the monitoring system has been designed to detect ‘gear tooth breakage’ by estimating ‘crack length’. The data-mining techniques used in the association phase have provided the information that ‘crack length’ is best estimated by a combination of a direct crack-length measurement and by debris analysis of the chip content in oil. The system has sensors placed in locations where these measurements can be taken. For brevity, let us follow the following notations: CL crack length measurement, CC chip content in oil,

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where CC is an indirect observable. Therefore, before using it to obtain a value of the estimator for ‘crack length’, we will convert CC into CLO (a representation of crack length) using a relationship between the two measurements CC, CLO. Thus, hypothetically, let us say that the following relationship exists: CLO ¼ Ki ðCC Þ j , where CLO is the ‘converted’ value of CC; and Ki and j are constants. Then based on the above, the crack length estimator L is a function of CL and CLO, or: L ¼ f ½CL ,CLO : The key now is to form a crisper mathematical expression of the above function. This can be performed, for instance, using a weighted linear combination using constants K1 and K2 such that: L ¼ K1 CL þ K2 CC : One approach for determining the constants could be calculating K1 and K2 so that the variance of the estimator L is at a minimum. Thus, the constants are determined with an objective function in mind. A similar model-based approach could be used to convert CC into CLO. Note that there is a crisp mathematical expression that represents the linear relationship between the estimator L and the measurements CL, CC.

3.6.1 Fuzzy sets in data fusion. Another interesting approach for combining information involves the use of fuzzy logic along with data fusion (Lingyu et al. 1999). The fuzzy process starts with the fuzzification of inputs (in the present case, the observables). This requires setting up membership functions for each observable. Essentially, a range of observable measurements and associated probabilities, or grades of membership, are mapped to a fuzzy set. For example, consider the ‘crack length measurement’ observable. Table 1 depicts a hypothetical mapping. The fuzzy sets are related to the possibility of failure based on crack length. The membership function for ‘crack length measurement’ (figure 6) is a graphical representation of table 1. Figure 6 also depicts the confidence level, or probability, of the mapping on a scale of 0 to 1. For instance, a crack length measurement of 15 mm has a 90% probability of still being considered ‘safe’. A similar function can be constructed for the other observable (‘chip content in oil’) (for an exhaustive treatment of fuzzy sets and membership functions, see Klir and Yuan 1995). The failure mode being estimated (‘gear bearing damage’) is also represented by fuzzy sets (e.g. minimum, moderate and maximum). Fuzzy rules are then designed Table 1.

Mapping measurements with fuzzy sets.

Measurement range (mm)

Fuzzy set

0–35 20–50 435

Safe Alert Danger


Figure 6.

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Membership function for crack length measurement.

and evaluated to relate the observable with the failure mode. A few hypothetical examples of fuzzy rules are as follows: . IF ‘chip content’ is safe, THEN ‘bearing damage’ is minimum. . IF ‘chip content’ is danger, THEN ‘bearing damage’ is maximum. Defuzzification is carried out in order to relate the above fuzzy relation into numeric values representing ‘bearing damage’ (for information on defuzzification techniques, see Klir and Yuan 1995). Once each observable value has been mapped to the fuzzy sets of the failure mode and related to the corresponding numeric values, a datafusion technique (e.g. an artificial intelligence approach such as neural networks) could be used to fuse the individual ‘refined’ observables into a combined estimate of ‘bearing damage’. 3.6.2 Higher level fusion. The estimation techniques described above have been carried out at the ‘component level’. Similar models can be used for estimation at higher levels in the fault tree. For example, say that the goal at the sub-systems level is to estimate the ‘seizure’ of a transmission unit. It is known from the fault tree that ‘seizure’ is influenced by ‘gear teeth breakage’ and ‘gear bearing failure’, which are failure modes or phenomena at the parts level. Then, a ‘seizure’ estimator can be determined by developing a model that combines or fuses the estimates of ‘gear teeth breakage’ and ‘gear bearing failure’. These estimates are, of course, fused information from lower-level sensors such as the vibration data or chip content in oil. 3.7 Data fusion procedure for CBM The above architecture can be used as a template for establishing a procedure for obtaining fused estimates in a CBM environment. Assuming that the need for a model-based approach has been identified, the goals for CBM formulated

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and the parts tree developed, the steps of the procedure can be summarized as follows: 1. Define the ith failure mode for a component/part. 2. Develop the candidate failure phenomena at the (i þ 1)st level for all related components/parts such that the failure phenomena at the (i þ 1)st level are related to that at level i. 3. Validate the candidate phenomena using association models. 4. Nominate estimation techniques for each of the phenomena. The estimates can be used for making replacement and maintenance decisions. 3.8 Decision support module The outcome of the estimate process is a representation of the condition, health and remaining life of the system under study. These results are presented to the user to choose from the available options for action and risk mitigation. Depending on the estimation technique, the process results may also include recommendations for the optimal course of action. This module directly corresponds to the OSA-CBM decision support module. 4. Conclusions and future work The overall objective of this paper was to try to weave together the data mining, data fusion and CBM. It was observed during the course of the explanation of the approach that the different modules complemented each other to form a truly integrated picture. On many occasions, there was an overlap of functions, e.g. signal detection and processing forms a part of data fusion and data mining as well as condition monitoring. The authors used their understanding of the overall process to demarcate the overlapping functions wherever necessary. The present paper has proposed the use of data-mining techniques for developing fault trees, planning the condition-monitoring activities and solving the association problem. Data mining provides a model-free approach that can help uncover relationships that may not be apparent using manual analysis. In fact, data mining and data fusion are being increasingly seen as compatible, complementary technologies that can be used to represent information in a manner that improves decision-making (Waltz 1998). It was also observed that data mining interacts with the condition-monitoring process by using the monitoring data to form associations. It subsequently provides feedback by updating ‘threshold’ or ‘alarm’ levels. These alarm levels are the sensor values that indicate an abnormality/failure in the system. The true worth of a conceptual design can only be appreciated if a corresponding implementation strategy that uses the design as a template is formulated. This is the future work that the authors envisage in the area. However, before implementation is contemplated, certain key design issues need to be tackled. The main design issues facing the CBM community are as follows: . Development of micro-mechanical failure models whose objectives would be as follows: . A detailed and accurate analysis of potential failures related to a particular component or assembly.


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. Modelling the influence of the failure of one component over the failure of another. This requires a comprehensive understanding of componentcomponent interaction. . Conceptualization of strategies for decision-making and goal setting across multiple ‘component’ levels and time periods. A methodology for incorporating cost as a factor to be considered in setting up these strategies is also required. Our next step is to translate the high-level design into a software implementation. An automated process is perhaps the best way to integrate the modules with each other and also integrate CBM with other business functions. In addition, data mining and data fusion, which are central to the architecture, are heavily dependent on information technology. Some of the considerations of a software design could be as follows: . Automating the process of identifying the systems that qualify for CBM, given an overall maintenance goal. . Constructing the parts tree based on the above identification. This may require integration of the CBM database with the enterprise resource planning (ERP) system because of the similarities between the parts tree and the BOM. Databases will have to be designed in a way that they provide relevant information to both the parts tree and the BOM, and at the same time avoid duplication of information. . Integration with the ERP system to enable parts ordering if required. It is hoped the interdisciplinary approach presented herein serves as a foundation on which implementation strategies for each individual module could be devised.

Acknowledgment The authors would like to express their gratitude to the Applied Research Laboratory at the Pennsylvania State University, Philadelphia, PA, USA, for sponsoring the initiative outlined in this paper.

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